Michael J. Pazzani Department of Information and Computer Science
University of California, Irvine, CA 92697
phone: (949) 824-5888 fax: (949) 824-4056
URL: http://www.ics.uci.edu/~pazzani
e-mail: pazzani@ics.uci.edu
Shankle, W.R., Mani, S., Pazzani, M. J. and Smyth, P. (1997). Use of a Computerized Patient Record Database of Normal Aging and Very Mildly Demented Subjects to Compare Classification Accuracies Obtained with Machine Learning Methods and Logistic Regression. Computing Science and Statistics, 29: 201-209.
Giovanni Semeraro, Floriana Esposito, Donato Malerba, Clifford Brunk,
Michael Pazzani: (1994) Avoiding Non-Termination when Learning Logical
Programs: A Case Study with FOIL and FOCL. In Laurent Fribourg, Franco
Turini (Eds.): Logic Programming Synthesis and Transformation -
Meta-Programming in Logic. 4th Internation Workshops, LOPSTR'94 and
META'94, Pisa, Italy, June 20-21, 1994, Proceedings. Lecture Notes in
Computer Science, Vol. 883, Springer.
Pazzani, M., Brunk, C., & Silverstein, G. (1992). A information-based approach to combining empirical and explanation-based learning. In S. Muggleton (Ed.). Inductive Logic Programming. (pp. 373-394). London: Academic Press.
Fisher, D., & Pazzani, M. (1991). Computational models of concept learning. In D. Fisher, M. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.
Fisher, D., Pazzani, M., & Langley, P. (1991). Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.
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Pazzani, M. (1991). Learning to predict and explain: An integration of similarity-based, theory-driven and explanation-based learning. Journal of the Learning Sciences, 1, 2, 153-199.
Pazzani, M., Brunk, C., & Silverstein, G. (1991). A knowledge-intensive approach to learning relational concepts. Proceedings of the Eighth International Workshop on Machine Learning (pp. 432-436). Evanston, IL: Morgan Kaufmann.
Fisher, D., & Pazzani, M. (1991). Theory-guided concept formation. In D. Fisher, M. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.
Fisher, D., & Pazzani, M. (1991). Concept formation in context. In D. Fisher, M. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.
Pazzani, M. (1990). Creating a memory of causal relationships: An integration of empirical and explanation-based learning methods. Hillsdale, NJ: Lawrence Erlbaum Associates.
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Pazzani, M. (1990). Learning in order to avoid search in logic programming. Computers and Mathematics with Applications, 2, 10, 101-110.
Pazzani M., & Dyer, M. (1990). Memory organization and explanation-based learning.International Journal of Expert Systems, 2, 3, 331-358.
Billman, D., Fisher, D., Gluck, M., Langley, P., & Pazzani, M. (1990). Computational models of category learning. Proceedings of the Twelfth Annual Conference of the CognitiveScience Society. (pp. 989-996). Cambridge, MA: Lawrence Erlbaum.
Pazzani, M. (1989). Explanation-based learning of diagnostic heuristics: A comparison of learning from success and failure. Artificial Intelligence Systems in Government Conference (pp. 164-169). Washington DC.
Pazzani, M., & Schulenburg, D. (1989).The influence of prior theories on the ease of concept acquisition. Proceedings of the Eleventh Annual Conference of the Cognitive Science Society (pp. 812-819). Ann Arbor, MI: Lawrence Erlbaum
Pazzani, M. (1989). Learning fault diagnosis heuristics from device descriptions. In Y. Kodratoff & R. Michalski (Eds.), Machine Learning: An artificial intelligence approach (Vol. III).San Mateo, CA: Morgan Kaufmann.
Pazzani, M., & Sarrett, W. (1989). Average case analysis of conjunctive learning algorithms. Proceedings of the Seventh International Conference on Machine Learning (pp. 339-347). Austin, TX: Morgan Kaufmann.
Pazzani, M. (1989). Creating high-level knowledge structures from simple elements. In K. Morik (Ed.), Knowledge representation and organization in machine learning, Lecture notes in Artificial Intelligence, No 347. New York: Springer-Verlag.
Pazzani, M. (1988). Explanation-based learning for knowledge-based systems. In B. Gaines & J. Boose (Eds.), Knowledge acquisition for knowledge-based systems (pp. 215-238). London: Academic Press.
Pazzani, M. (1988). Learning during plan recognition. AAAI Workshop on Plan Recognition. (pp. 1-5).St. Paul, MN.
Pazzani, M. (1988). Integrated learning with incorrect and incomplete theories. Proceedings of the Fifth International Conference on Machine Learning (pp. 291-298). Ann Arbor, MI: Morgan Kaufmann.
Pazzani, M. (1988). Integrating empirical and explanation-based learning methods in OCCAM. Proceedings of the Third European Working Session on Learning (pp. 147-166). Glasgow, Scotland: Pitman.
Pazzani, M. (1987). Failure-driven learning of fault diagnosis heuristics. IEEE Transactions on Systems, Man and Cybernetics: Special issue on Causal and Strategic Aspects of Diagnostic Reasoning, 17, 3, 380-394.
Pazzani, M. (1987). Explanation-based learning for knowledge-based systems. International Journal of Man-Machine Studies, 26, 413-433.
Pazzani, M. (1985). Explanation and generalization-based memory. Proceedings of the Seventh Annual Conference of the Cognitive Society Conference (pp. 323-328). Irvine, CA: Lawrence Erlbaum.